inference_args_en.md

PaddleOCR Model Inference Parameter Explanation

When using PaddleOCR for model inference, you can customize the modification parameters to modify the model, data, preprocessing, postprocessing, etc.（parameter file：utility.py），The detailed parameter explanation is as follows:

• Global parameters

parameters	type	default	implication
image_dir	str	None, must be specified explicitly	Image or folder path
page_num	int	0	Valid when the input type is pdf file, specify to predict the previous page_num pages, all pages are predicted by default
vis_font_path	str	"./doc/fonts/simfang.ttf"	font path for visualization
drop_score	float	0.5	Results with a recognition score less than this value will be discarded and will not be returned as results
use_pdserving	bool	False	Whether to use Paddle Serving for prediction
warmup	bool	False	Whether to enable warmup, this method can be used when statistical prediction time
draw_img_save_dir	str	"./inference_results"	The saving folder of the system's tandem prediction OCR results
save_crop_res	bool	False	Whether to save the recognized text image for OCR
crop_res_save_dir	str	"./output"	Save the text image path recognized by OCR
use_mp	bool	False	Whether to enable multi-process prediction
total_process_num	int	6	The number of processes, which takes effect when use_mp is True
process_id	int	0	The id number of the current process, no need to modify it yourself
benchmark	bool	False	Whether to enable benchmark, and make statistics on prediction speed, memory usage, etc.
save_log_path	str	"./log_output/"	Folder where log results are saved when benchmark is enabled
show_log	bool	True	Whether to show the log information in the inference
use_onnx	bool	False	Whether to enable onnx prediction

• Prediction engine related parameters

parameters	type	default	implication
use_gpu	bool	True	Whether to use GPU for prediction
ir_optim	bool	True	Whether to analyze and optimize the calculation graph. The prediction process can be accelerated when ir_optim is enabled
use_tensorrt	bool	False	Whether to enable tensorrt
min_subgraph_size	int	15	The minimum subgraph size in tensorrt. When the size of the subgraph is greater than this value, it will try to use the trt engine to calculate the subgraph.
precision	str	fp32	The precision of prediction, supports fp32, fp16, int8
enable_mkldnn	bool	True	Whether to enable mkldnn
cpu_threads	int	10	When mkldnn is enabled, the number of threads predicted by the cpu

• Text detection model related parameters

parameters	type	default	implication
det_algorithm	str	"DB"	Text detection algorithm name, currently supports DB, EAST, SAST, PSE, DB++, FCE
det_model_dir	str	xx	Detection inference model paths
det_limit_side_len	int	960	image side length limit
det_limit_type	str	"max"	The side length limit type, currently supports minand max. min means to ensure that the shortest side of the image is not less than det_limit_side_len, max means to ensure that the longest side of the image is not greater than det_limit_side_len

The relevant parameters of the DB algorithm are as follows

parameters	type	default	implication
det_db_thresh	float	0.3	In the probability map output by DB, only pixels with a score greater than this threshold will be considered as text pixels
det_db_box_thresh	float	0.6	Within the detection box, when the average score of all pixels is greater than the threshold, the result will be considered as a text area
det_db_unclip_ratio	float	1.5	The expansion factor of the Vatti clipping algorithm, which is used to expand the text area
max_batch_size	int	10	max batch size
use_dilation	bool	False	Whether to inflate the segmentation results to obtain better detection results
det_db_score_mode	str	"fast"	DB detection result score calculation method, supports fast and slow, fast calculates the average score according to all pixels within the bounding rectangle of the polygon, slow calculates the average score according to all pixels within the original polygon, The calculation speed is relatively slower, but more accurate.

The relevant parameters of the EAST algorithm are as follows

parameters	type	default	implication
det_east_score_thresh	float	0.8	Threshold for score map in EAST postprocess
det_east_cover_thresh	float	0.1	Average score threshold for text boxes in EAST postprocess
det_east_nms_thresh	float	0.2	Threshold of nms in EAST postprocess

The relevant parameters of the SAST algorithm are as follows

parameters	type	default	implication
det_sast_score_thresh	float	0.5	Score thresholds in SAST postprocess
det_sast_nms_thresh	float	0.5	Thresholding of nms in SAST postprocess
det_sast_polygon	bool	False	Whether polygon detection, curved text scene (such as Total-Text) is set to True

The relevant parameters of the PSE algorithm are as follows

parameters	type	default	implication
det_pse_thresh	float	0.0	Threshold for binarizing the output image
det_pse_box_thresh	float	0.85	Threshold for filtering boxes, below this threshold is discarded
det_pse_min_area	float	16	The minimum area of the box, below this threshold is discarded
det_pse_box_type	str	"box"	The type of the returned box, box: four point coordinates, poly: all point coordinates of the curved text
det_pse_scale	int	1	The ratio of the input image relative to the post-processed image, such as an image of 640*640, the network output is 160*160, and when the scale is 2, the shape of the post-processed image is 320*320. Increasing this value can speed up the post-processing speed, but it will bring about a decrease in accuracy

• Text recognition model related parameters

parameters	type	default	implication
rec_algorithm	str	"CRNN"	Text recognition algorithm name, currently supports CRNN, SRN, RARE, NETR, SAR, ViTSTR, ABINet, VisionLAN, SPIN, RobustScanner, SVTR, SVTR_LCNet
rec_model_dir	str	None, it is required if using the recognition model	recognition inference model paths
rec_image_shape	list	[3, 48, 320]	Image size at the time of recognition
rec_batch_num	int	6	batch size
max_text_length	int	25	The maximum length of the recognition result, valid in SRN
rec_char_dict_path	str	"./ppocr/utils/ppocr_keys_v1.txt"	character dictionary file
use_space_char	bool	True	Whether to include spaces, if True, the space character will be added at the end of the character dictionary

• End-to-end text detection and recognition model related parameters

parameters	type	default	implication
e2e_algorithm	str	"PGNet"	End-to-end algorithm name, currently supports PGNet
e2e_model_dir	str	None, it is required if using the end-to-end model	end-to-end model inference model path
e2e_limit_side_len	int	768	End-to-end input image side length limit
e2e_limit_type	str	"max"	End-to-end side length limit type, currently supports min and max. min means to ensure that the shortest side of the image is not less than e2e_limit_side_len, max means to ensure that the longest side of the image is not greater than e2e_limit_side_len
e2e_pgnet_score_thresh	float	0.5	End-to-end score threshold, results below this threshold are discarded
e2e_char_dict_path	str	"./ppocr/utils/ic15_dict.txt"	Recognition dictionary file path
e2e_pgnet_valid_set	str	"totaltext"	The name of the validation set, currently supports totaltext, partvgg, the post-processing methods corresponding to different data sets are different, and it can be consistent with the training process
e2e_pgnet_mode	str	"fast"	PGNet's detection result score calculation method, supports fast and slow, fast calculates the average score according to all pixels within the bounding rectangle of the polygon, slow calculates the average score according to all pixels within the original polygon, The calculation speed is relatively slower, but more accurate.

• Angle classifier model related parameters

parameters	type	default	implication
use_angle_cls	bool	False	whether to use an angle classifier
cls_model_dir	str	None, if you need to use, you must specify the path explicitly	angle classifier inference model path
cls_image_shape	list	[3, 48, 192]	prediction shape
label_list	list	['0', '180']	The angle value corresponding to the class id
cls_batch_num	int	6	batch size
cls_thresh	float	0.9	Prediction threshold, when the model prediction result is 180 degrees, and the score is greater than the threshold, the final prediction result is considered to be 180 degrees and needs to be flipped